Exploratory Data Analysis of League of Legends World Championship Games from 2016-2022

1. Aims, Objectives and Background

1.1 Introduction

League of Legends (LOL), is commonly referred to as League, is a Multiplayer Online Battle Arena (MOBA) video game developed and published by Riot Games (Riot Games, 2022). It is a team-based strategy game where two teams (red and blue), consisting of 5 champions on each side, face off to destroy the other's base. There are 3 lanes, a jungle and 5 roles in total, each player having their own role. Every year, there is a World Championship held, whereby every region will send in their best teams to compete, to determine the best team not only in their region, but in the world. (Wikipedia, 2022) I am an avid gamer myself, and I have been playing League for many years. With the new skills that I have learnt from this course so far, I would like to do some analysis into the World Championship games over the years, to be able to ascertain the direction that the game is moving towards.

1.2 Aims and Objectives

Within this project, I would like to explore the following:
    Analysis of game length over the different years
    Win Rates for Blue and Red side over the different years
    Win Rates with different first objectives taken
    Gold differential at 15 minutes for games won over the different years

For this project, my aims are to:

    Decide how much data is required to sufficiently explore the above questions
    Find and obtain a data from the web, preferably in a form which allows for data cleaning and analysis
    Perform data cleaning and transformation such that it is suitable for use in analysis
    Carry out some data analysis on the cleaned data, which will aid in my exploration of the topic

2. Dataset Used

2.1 Choice of Data

I have found a data from Oracle's Elixir, which is a site that publishes competitive League of Legends datasets, and it is in a csv format, which makes it easier for me to work with. The dataset consists of every competitive game that has been played during the season of each year. I decided to use this data as it contains data from several different sources, which are then compiled together.

2.2 Limitations and constraints of the data

2.2.1 Difference in Calculations

There might be non-matching numbers with this dataset to a different dataset due to difference in the way the values are calculated. For example, the author of the dataset mentioned that certain broadcasts of the game calculates the Damage Share (DMG%) on adding up the totals of all games, then dividing, which causes longer games to influence the results more than shorter games. The author however, calculates it by separately calculating a player's DMG% for each individual game, then averaging across all games.

2.2.2 Certain games being incomplete

In the dataset, there is a column which is labelled as 'data completeness', and there are certain rows whereby it is incomplete, therefore it might affect my analysis. In this project, I will clean the data and figure out if it affects my analysis.

2.3 Ethical Considerations

2.3.1 Use of dataset from all competitive games played

Riot Games has a website with their API being open-sourced, whereby we are able to use it to obtain statistics from active games, match history, ranked statistics, and many more (Riot Games, 2022).

Under the website, it states that:

"Riot grants to You a limited, non-exlusive, non-assignable, non-sublicensable, non-transferable, revocable right to access and use the Materials owned by Riot in order to develop and distribute Applications that incorporate, access or use any Game Information, for the purpose of displaying such Game Information to end users of such Applications."

2.3.2 Onward use / reuseage of data and derived data

The Riot Games API website also mentions that we are prohibited from using the Riot Games API to collect any additional information about the Game or Game users that is not provided Game Information or permitted by the Riot Games API.

All the data and game information that we are using is taken from OraclesElixir, which uses the Riot Games API, and analysis and conclusions are my own.

3. Importing the libraries required for my project

In [1]:
# Import general libraries
import numpy as np
import pandas as pd
pd.options.mode.chained_assignment = None

# Import plotting libraries
import plotly.express as px
import plotly.graph_objects as go

3.1 Obtaining the Datasets

The datasets are obtained from Oracle's Elixir, which is a source for League of Legends esports statistics. They have compiled data from several different sources, mainly the Riot Games API, and others including lolesports.com. They have a downloads page, which consists of the compiled data from the different years individually.

3.1.1 Importing the datasets

In [2]:
# Parsing the csv files to be read
WCdata2022 = pd.read_csv("2022_LoL_esports_match_data_from_OraclesElixir.csv", dtype='unicode')
WCdata2021 = pd.read_csv("2021_LoL_esports_match_data_from_OraclesElixir.csv", dtype='unicode')
WCdata2020 = pd.read_csv("2020_LoL_esports_match_data_from_OraclesElixir.csv", dtype='unicode')
WCdata2019 = pd.read_csv("2019_LoL_esports_match_data_from_OraclesElixir.csv", dtype='unicode')
WCdata2018 = pd.read_csv("2018_LoL_esports_match_data_from_OraclesElixir.csv", dtype='unicode')
WCdata2017 = pd.read_csv("2017_LoL_esports_match_data_from_OraclesElixir.csv", dtype='unicode')
WCdata2016 = pd.read_csv("2016_LoL_esports_match_data_from_OraclesElixir.csv", dtype='unicode')

4. Data Cleaning

4.1 Filtering the dataset

Now, let us look at the dataset's features
In [3]:
#Looking at the rows and columns in the datasets
data2022shape = WCdata2022.shape
data2021shape = WCdata2021.shape
data2020shape = WCdata2020.shape
data2019shape = WCdata2019.shape
data2018shape = WCdata2018.shape
data2017shape = WCdata2017.shape
data2016shape = WCdata2016.shape

print(data2022shape, data2021shape, data2020shape, data2019shape, data2018shape, data2017shape, data2016shape)

(1860, 123) (1452, 123) (1368, 123) (1440, 123) (1428, 123) (1428, 123) (924, 123)
From this, we are able to tell that the datasets have the same amount of columns/features (123), though different amount of rows, as there are more games played with every season. The dataset has a datacompleteness column, which determines if the data is complete or not. We will use that to filter the complete datas, as the incomplete data might cause implications to our analysis.
In [4]:
#Filtering the datasets into the completed data, removing the incomplete games
CompleteData2022 = WCdata2022[WCdata2022['datacompleteness'] == "complete"]
CompleteData2021 = WCdata2021[WCdata2021['datacompleteness'] == "complete"]
CompleteData2020 = WCdata2020[WCdata2020['datacompleteness'] == "complete"]
CompleteData2019 = WCdata2019[WCdata2019['datacompleteness'] == "complete"]
CompleteData2018 = WCdata2018[WCdata2018['datacompleteness'] == "complete"]
CompleteData2017 = WCdata2017[WCdata2017['datacompleteness'] == "complete"]
CompleteData2016 = WCdata2016[WCdata2016['datacompleteness'] == "complete"]
In [5]:
#Checking if there are any missing/empty values in any column, and sum them all up
data2022missing = WCdata2022.isna().sum()
data2021missing = WCdata2021.isna().sum()
data2020missing = WCdata2020.isna().sum()
data2019missing = WCdata2019.isna().sum()
data2018missing = WCdata2018.isna().sum()
data2017missing = WCdata2017.isna().sum()
data2016missing = WCdata2016.isna().sum()

print(data2022missing, "\n")
print(data2021missing, "\n")
print(data2020missing, "\n")
print(data2019missing, "\n")
print(data2018missing, "\n")
print(data2017missing, "\n")
print(data2016missing, "\n")

gameid                 0
datacompleteness       0
url                 1692
league                 0
year                   0
                    ... 
assistsat15          168
deathsat15           168
opp_killsat15        168
opp_assistsat15      168
opp_deathsat15       168
Length: 123, dtype: int64 

gameid              0
datacompleteness    0
url                 0
league              0
year                0
                   ..
assistsat15         0
deathsat15          0
opp_killsat15       0
opp_assistsat15     0
opp_deathsat15      0
Length: 123, dtype: int64 

gameid              0
datacompleteness    0
url                 0
league              0
year                0
                   ..
assistsat15         0
deathsat15          0
opp_killsat15       0
opp_assistsat15     0
opp_deathsat15      0
Length: 123, dtype: int64 

gameid              0
datacompleteness    0
url                 0
league              0
year                0
                   ..
assistsat15         0
deathsat15          0
opp_killsat15       0
opp_assistsat15     0
opp_deathsat15      0
Length: 123, dtype: int64 

gameid              0
datacompleteness    0
url                 0
league              0
year                0
                   ..
assistsat15         0
deathsat15          0
opp_killsat15       0
opp_assistsat15     0
opp_deathsat15      0
Length: 123, dtype: int64 

gameid              0
datacompleteness    0
url                 0
league              0
year                0
                   ..
assistsat15         0
deathsat15          0
opp_killsat15       0
opp_assistsat15     0
opp_deathsat15      0
Length: 123, dtype: int64 

gameid              0
datacompleteness    0
url                 0
league              0
year                0
                   ..
assistsat15         0
deathsat15          0
opp_killsat15       0
opp_assistsat15     0
opp_deathsat15      0
Length: 123, dtype: int64
From the very first result, we can see that there are missing values around.However, before removing the values, we will want to further filter the dataset. The way the dataset is structured is that they will show the players from each team, then an extra 2 rows for the combined team score. The 'playername' column for the extra 2 rows will be empty, therefore we cannot remove the empty rows first, we will have to filter the dataset into the 'position' where it has team, as we are looking at the overall team values.
In [6]:
#Filtering the dataset into just the team variables
CompleteTeamData2022 = CompleteData2022[CompleteData2022['position'] == "team"]
CompleteTeamData2021 = CompleteData2021[CompleteData2021['position'] == "team"]
CompleteTeamData2020 = CompleteData2020[CompleteData2020['position'] == "team"]
CompleteTeamData2019 = CompleteData2019[CompleteData2019['position'] == "team"]
CompleteTeamData2018 = CompleteData2018[CompleteData2018['position'] == "team"]
CompleteTeamData2017 = CompleteData2017[CompleteData2017['position'] == "team"]
CompleteTeamData2016 = CompleteData2016[CompleteData2016['position'] == "team"]

4.2 Creating a new Dataset with the columns that I am interested in

Now, I will continue to clean the data. As there are many columns that I am not interested in, I would pull out the columns that is useful in my current analysis, or those that I am interested it.
In [7]:
CleanedData2022 = CompleteTeamData2022.loc(axis=1)["year", "date", "side", "teamname", "gamelength", "result", "firstdragon",
                                       "firstherald", "golddiffat15"]

CleanedData2021 = CompleteTeamData2021.loc(axis=1)["year", "date", "side", "teamname", "gamelength", "result", "firstdragon",
                                       "firstherald", "golddiffat15"]

CleanedData2020 = CompleteTeamData2020.loc(axis=1)["year", "date", "side", "teamname", "gamelength", "result", "firstdragon",
                                       "firstherald", "golddiffat15"]

CleanedData2019 = CompleteTeamData2019.loc(axis=1)["year", "date", "side", "teamname", "gamelength", "result", "firstdragon",
                                       "firstherald", "golddiffat15"]

CleanedData2018 = CompleteTeamData2018.loc(axis=1)["year", "date", "side", "teamname", "gamelength", "result", "firstdragon",
                                       "firstherald", "golddiffat15"]

CleanedData2017 = CompleteTeamData2017.loc(axis=1)["year", "date", "side", "teamname", "gamelength", "result", "firstdragon",
                                       "firstherald", "golddiffat15"]

CleanedData2016 = CompleteTeamData2016.loc(axis=1)["year", "date", "side", "teamname", "gamelength", "result", "firstdragon",
                                       "firstherald", "golddiffat15"]

4.2.1 Checking for missing variables

Moving on, I will check to see if there are any missing values in the dataframes.
In [8]:
CleanedData2022Missing = CleanedData2022.isna().sum()
CleanedData2021Missing = CleanedData2021.isna().sum()
CleanedData2020Missing = CleanedData2020.isna().sum()
CleanedData2019Missing = CleanedData2019.isna().sum()
CleanedData2018Missing = CleanedData2018.isna().sum()
CleanedData2017Missing = CleanedData2017.isna().sum()
CleanedData2016Missing = CleanedData2016.isna().sum()

print(CleanedData2022Missing, "\n")
print(CleanedData2021Missing, "\n")
print(CleanedData2020Missing, "\n")
print(CleanedData2019Missing, "\n")
print(CleanedData2018Missing, "\n")
print(CleanedData2017Missing, "\n")
print(CleanedData2016Missing, "\n")

year            0
date            0
side            0
teamname        0
gamelength      0
result          0
firstdragon     0
firstherald     0
golddiffat15    0
dtype: int64 

year            0
date            0
side            0
teamname        0
gamelength      0
result          0
firstdragon     0
firstherald     0
golddiffat15    0
dtype: int64 

year            0
date            0
side            0
teamname        0
gamelength      0
result          0
firstdragon     0
firstherald     0
golddiffat15    0
dtype: int64 

year            0
date            0
side            0
teamname        0
gamelength      0
result          0
firstdragon     0
firstherald     0
golddiffat15    0
dtype: int64 

year            0
date            0
side            0
teamname        0
gamelength      0
result          0
firstdragon     0
firstherald     0
golddiffat15    0
dtype: int64 

year            0
date            0
side            0
teamname        0
gamelength      0
result          0
firstdragon     2
firstherald     0
golddiffat15    0
dtype: int64 

year            0
date            0
side            0
teamname        0
gamelength      0
result          0
firstdragon     0
firstherald     0
golddiffat15    0
dtype: int64
We can see from the printed results above that there is 2 missing values under the firstdragon column of the year 2017. I will now check the row to see what is the missing value
In [9]:
#Filter the nan_values from the 2017 data and save it to a variable then print it out to see the row
nan_values = CleanedData2017[CleanedData2017.isna().any(axis=1)]

nan_values
Out[9]:
year date side teamname gamelength result firstdragon firstherald golddiffat15
442 2017 9/29/2017 6:52 Blue Team WE 1520 1 NaN 1 1949
443 2017 9/29/2017 6:52 Red Young Generation 1520 0 NaN 0 -1949
With this, I went to search on the web to check what was the issue

2017%20YG%20v%20WE%20Game%202.png

I found this picture, which is a screenshot taken of the game right before it ended, and there was dragons taken in this game (Game 2), which I have indicated with a red box on the top of the image.

2017%20YG%20v%20WE%20Game%201.png

I have also found another picture of Game 1, which shows that there is no dragon taken in this game, I have also indicated with a red box. Therefore, with this, I decided to fill the NA values with a 0, as there was 0 dragons taken in this game.
In [10]:
#Replacing the NAN values with a 0
CleanedData2017 = CleanedData2017.fillna(0)

#Re-checking if there are any more NA values in the 2017 data
CleanedData2017.isna().sum()
Out[10]:
year            0
date            0
side            0
teamname        0
gamelength      0
result          0
firstdragon     0
firstherald     0
golddiffat15    0
dtype: int64
Now that there are no missing values, we can start working on merging the datasets together, and plotting our graphs

4.3 Merging the data together

In [11]:
#Combining the datasets using the pandas concat function
CombinedData = pd.concat([CleanedData2022, CleanedData2021, CleanedData2020, CleanedData2019, CleanedData2018, 
                          CleanedData2017, CleanedData2016])

#Displaying the combined dataset
with pd.option_context("display.max_rows", None, "display.max_columns", None):
    display(CombinedData)
year date side teamname gamelength result firstdragon firstherald golddiffat15
10 2022 9/1/2022 7:08 Blue DWG KIA 1943 1 1 1 4259
11 2022 9/1/2022 7:08 Red Liiv SANDBOX 1943 0 0 0 -4259
22 2022 9/1/2022 8:42 Blue Liiv SANDBOX 1989 1 1 1 1583
23 2022 9/1/2022 8:42 Red DWG KIA 1989 0 0 0 -1583
34 2022 9/1/2022 9:38 Blue DWG KIA 1720 1 1 1 1043
35 2022 9/1/2022 9:38 Red Liiv SANDBOX 1720 0 0 0 -1043
46 2022 9/1/2022 10:31 Blue Liiv SANDBOX 2217 0 1 1 176
47 2022 9/1/2022 10:31 Red DWG KIA 2217 1 0 0 -176
58 2022 9/2/2022 6:28 Blue KT Rolster 2484 0 1 1 3186
59 2022 9/2/2022 6:28 Red DRX 2484 1 0 0 -3186
82 2022 9/2/2022 8:47 Blue DRX 1585 0 0 1 -1365
83 2022 9/2/2022 8:47 Red KT Rolster 1585 1 1 0 1365
106 2022 9/2/2022 9:37 Blue DRX 1867 0 0 1 -1522
107 2022 9/2/2022 9:37 Red KT Rolster 1867 1 1 0 1522
130 2022 9/2/2022 10:30 Blue DRX 1922 1 0 0 1576
131 2022 9/2/2022 10:30 Red KT Rolster 1922 0 1 1 -1576
154 2022 9/2/2022 11:23 Blue KT Rolster 2052 0 0 1 -28
155 2022 9/2/2022 11:23 Red DRX 2052 1 1 0 28
178 2022 9/3/2022 7:12 Blue Liiv SANDBOX 2237 0 0 0 155
179 2022 9/3/2022 7:12 Red DRX 2237 1 1 1 -155
190 2022 9/3/2022 8:49 Blue Liiv SANDBOX 2246 1 1 1 2268
191 2022 9/3/2022 8:49 Red DRX 2246 0 0 0 -2268
214 2022 9/3/2022 9:49 Blue DRX 1789 0 0 0 -1433
215 2022 9/3/2022 9:49 Red Liiv SANDBOX 1789 1 1 1 1433
226 2022 9/3/2022 10:41 Blue DRX 2149 1 1 0 2041
227 2022 9/3/2022 10:41 Red Liiv SANDBOX 2149 0 0 1 -2041
262 2022 9/3/2022 11:38 Blue Liiv SANDBOX 1412 0 0 0 -5179
263 2022 9/3/2022 11:38 Red DRX 1412 1 1 1 5179
346 2022 9/29/2022 20:35 Blue Isurus 2278 0 0 0 -2243
347 2022 9/29/2022 20:35 Red MAD Lions 2278 1 1 1 2243
358 2022 9/29/2022 21:23 Blue Fnatic 1767 1 0 1 2961
359 2022 9/29/2022 21:23 Red Evil Geniuses 1767 0 1 0 -2961
370 2022 9/29/2022 23:09 Blue LOUD 1723 0 0 1 -1680
371 2022 9/29/2022 23:09 Red Beyond Gaming 1723 1 1 0 1680
382 2022 9/30/2022 0:11 Blue MAD Lions 2228 1 0 1 3509
383 2022 9/30/2022 0:11 Red Ä°stanbul Wildcats 2228 0 1 0 -3509
394 2022 9/30/2022 1:46 Blue Chiefs Esports Club 1792 0 1 1 -573
395 2022 9/30/2022 1:46 Red Fnatic 1792 1 0 0 573
406 2022 9/30/2022 2:27 Blue DetonatioN FocusMe 1381 0 0 1 -1665
407 2022 9/30/2022 2:27 Red LOUD 1381 1 1 0 1665
418 2022 9/30/2022 3:31 Blue Saigon Buffalo 2077 1 0 0 27
419 2022 9/30/2022 3:31 Red Ä°stanbul Wildcats 2077 0 1 1 -27
430 2022 9/30/2022 4:40 Blue DRX 2532 1 0 1 -1407
431 2022 9/30/2022 4:40 Red Royal Never Give Up 2532 0 1 0 1407
442 2022 9/30/2022 18:55 Blue Fnatic 1967 1 0 0 -1257
443 2022 9/30/2022 18:55 Red DetonatioN FocusMe 1967 0 1 1 1257
454 2022 9/30/2022 21:13 Blue Evil Geniuses 2126 1 0 0 -1527
455 2022 9/30/2022 21:13 Red LOUD 2126 0 1 1 1527
466 2022 9/30/2022 22:08 Blue Saigon Buffalo 1752 1 1 1 2281
467 2022 9/30/2022 22:08 Red Isurus 1752 0 0 0 -2281
478 2022 9/30/2022 22:59 Blue DetonatioN FocusMe 1834 1 1 1 4407
479 2022 9/30/2022 22:59 Red Chiefs Esports Club 1834 0 0 0 -4407
490 2022 9/30/2022 23:56 Blue Evil Geniuses 1945 1 0 1 4931
491 2022 9/30/2022 23:56 Red Beyond Gaming 1945 0 1 0 -4931
502 2022 10/1/2022 0:55 Blue DRX 1496 1 1 0 1841
503 2022 10/1/2022 0:55 Red Saigon Buffalo 1496 0 0 1 -1841
514 2022 10/1/2022 1:46 Blue MAD Lions 1957 0 0 1 -3791
515 2022 10/1/2022 1:46 Red Royal Never Give Up 1957 1 1 0 3791
526 2022 10/1/2022 2:48 Blue Ä°stanbul Wildcats 1588 0 0 0 -6675
527 2022 10/1/2022 2:48 Red DRX 1588 1 1 1 6675
538 2022 10/1/2022 19:29 Blue LOUD 1701 1 1 1 1573
539 2022 10/1/2022 19:29 Red Fnatic 1701 0 0 0 -1573
550 2022 10/1/2022 20:42 Blue MAD Lions 2088 1 0 0 954
551 2022 10/1/2022 20:42 Red Saigon Buffalo 2088 0 1 1 -954
562 2022 10/1/2022 21:49 Blue Beyond Gaming 2390 0 0 1 1903
563 2022 10/1/2022 21:49 Red DetonatioN FocusMe 2390 1 1 0 -1903
574 2022 10/1/2022 22:54 Blue Evil Geniuses 1527 1 1 1 3372
575 2022 10/1/2022 22:54 Red Chiefs Esports Club 1527 0 0 0 -3372
586 2022 10/1/2022 23:44 Blue Royal Never Give Up 1567 1 1 1 7503
587 2022 10/1/2022 23:44 Red Isurus 1567 0 0 0 -7503
598 2022 10/2/2022 0:37 Blue Chiefs Esports Club 1541 0 1 0 -1877
599 2022 10/2/2022 0:37 Red Beyond Gaming 1541 1 0 1 1877
610 2022 10/2/2022 1:27 Blue Royal Never Give Up 1848 1 1 1 3527
611 2022 10/2/2022 1:27 Red Ä°stanbul Wildcats 1848 0 0 0 -3527
622 2022 10/2/2022 2:25 Blue Isurus 1355 0 1 0 -7246
623 2022 10/2/2022 2:25 Red DRX 1355 1 0 1 7246
634 2022 10/2/2022 19:36 Blue Beyond Gaming 1970 0 1 1 -1903
635 2022 10/2/2022 19:36 Red Fnatic 1970 1 0 0 1903
646 2022 10/2/2022 20:47 Blue LOUD 1731 1 0 1 4638
647 2022 10/2/2022 20:47 Red Chiefs Esports Club 1731 0 1 0 -4638
658 2022 10/2/2022 21:41 Blue DetonatioN FocusMe 2413 1 0 1 1143
659 2022 10/2/2022 21:41 Red Evil Geniuses 2413 0 1 0 -1143
670 2022 10/2/2022 22:51 Blue Ä°stanbul Wildcats 1875 0 0 1 -1672
671 2022 10/2/2022 22:51 Red Isurus 1875 1 1 0 1672
682 2022 10/2/2022 23:47 Blue DRX 2023 1 0 0 -2421
683 2022 10/2/2022 23:47 Red MAD Lions 2023 0 1 1 2421
694 2022 10/3/2022 0:46 Blue Royal Never Give Up 1979 1 1 1 1900
695 2022 10/3/2022 0:46 Red Saigon Buffalo 1979 0 0 0 -1900
706 2022 10/3/2022 1:43 Blue Evil Geniuses 1712 1 0 1 2856
707 2022 10/3/2022 1:43 Red DetonatioN FocusMe 1712 0 1 0 -2856
718 2022 10/3/2022 2:39 Blue LOUD 1655 0 0 0 -3491
719 2022 10/3/2022 2:39 Red Evil Geniuses 1655 1 1 1 3491
730 2022 10/3/2022 17:07 Blue MAD Lions 1916 1 0 1 1063
731 2022 10/3/2022 17:07 Red Saigon Buffalo 1916 0 1 0 -1063
742 2022 10/3/2022 18:50 Blue MAD Lions 1477 0 0 0 -4601
743 2022 10/3/2022 18:50 Red Saigon Buffalo 1477 1 1 1 4601
754 2022 10/3/2022 19:32 Blue Saigon Buffalo 1401 0 1 0 -3938
755 2022 10/3/2022 19:32 Red MAD Lions 1401 1 0 1 3938
766 2022 10/3/2022 20:20 Blue Saigon Buffalo 2005 0 0 0 -2060
767 2022 10/3/2022 20:20 Red MAD Lions 2005 1 1 1 2060
778 2022 10/3/2022 21:16 Blue LOUD 2688 1 1 1 -504
779 2022 10/3/2022 21:16 Red DetonatioN FocusMe 2688 0 0 0 504
790 2022 10/3/2022 22:28 Blue DetonatioN FocusMe 1888 1 1 1 2184
791 2022 10/3/2022 22:28 Red LOUD 1888 0 0 0 -2184
802 2022 10/3/2022 23:23 Blue LOUD 1753 0 1 1 -824
803 2022 10/3/2022 23:23 Red DetonatioN FocusMe 1753 1 0 0 824
814 2022 10/4/2022 0:21 Blue LOUD 1560 0 1 0 -2232
815 2022 10/4/2022 0:21 Red DetonatioN FocusMe 1560 1 0 1 2232
826 2022 10/4/2022 18:11 Blue Evil Geniuses 2265 1 0 1 1513
827 2022 10/4/2022 18:11 Red MAD Lions 2265 0 1 0 -1513
838 2022 10/4/2022 18:58 Blue MAD Lions 1449 0 0 1 -2377
839 2022 10/4/2022 18:58 Red Evil Geniuses 1449 1 1 0 2377
850 2022 10/4/2022 19:42 Blue Evil Geniuses 1718 1 1 0 2048
851 2022 10/4/2022 19:42 Red MAD Lions 1718 0 0 1 -2048
862 2022 10/4/2022 20:37 Blue Royal Never Give Up 2202 0 0 1 858
863 2022 10/4/2022 20:37 Red DetonatioN FocusMe 2202 1 1 0 -858
874 2022 10/4/2022 21:37 Blue Royal Never Give Up 1955 1 0 0 -2619
875 2022 10/4/2022 21:37 Red DetonatioN FocusMe 1955 0 1 1 2619
886 2022 10/4/2022 22:57 Blue Royal Never Give Up 2122 1 0 1 1985
887 2022 10/4/2022 22:57 Red DetonatioN FocusMe 2122 0 1 0 -1985
898 2022 10/4/2022 23:46 Blue DetonatioN FocusMe 1627 0 0 0 -6029
899 2022 10/4/2022 23:46 Red Royal Never Give Up 1627 1 1 1 6029
910 2022 10/7/2022 19:36 Blue Cloud9 1943 0 0 1 1317
911 2022 10/7/2022 19:36 Red Fnatic 1943 1 1 0 -1317
922 2022 10/7/2022 21:56 Blue G2 Esports 1947 0 0 1 -4376
923 2022 10/7/2022 21:56 Red DWG KIA 1947 1 1 0 4376
934 2022 10/7/2022 22:53 Blue CTBC Flying Oyster 1911 1 1 0 2338
935 2022 10/7/2022 22:53 Red 100 Thieves 1911 0 0 1 -2338
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226 2019 10/4/2019 16:24 Blue MAMMOTH 1703 0 1 1 -3405
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239 2019 10/4/2019 17:24 Red Unicorns of Love.CIS 1862 0 0 0 -5368
250 2019 10/5/2019 9:36 Blue DWG KIA 1837 1 1 0 2710
251 2019 10/5/2019 9:36 Red Royal Youth 1837 0 0 1 -2710
262 2019 10/5/2019 10:49 Blue Hong Kong Attitude 2365 1 1 0 882
263 2019 10/5/2019 10:49 Red MEGA 2365 0 0 1 -882
274 2019 10/5/2019 11:45 Blue Flamengo Esports 1692 0 0 1 188
275 2019 10/5/2019 11:45 Red DWG KIA 1692 1 1 0 -188
286 2019 10/5/2019 12:36 Blue Lowkey Esports.Vietnam 2049 1 1 0 429
287 2019 10/5/2019 12:36 Red MEGA 2049 0 0 1 -429
298 2019 10/5/2019 13:33 Blue Flamengo Esports 2434 0 1 0 -1175
299 2019 10/5/2019 13:33 Red Royal Youth 2434 1 0 1 1175
310 2019 10/5/2019 14:36 Blue Lowkey Esports.Vietnam 1822 0 0 1 -3777
311 2019 10/5/2019 14:36 Red Hong Kong Attitude 1822 1 1 0 3777
322 2019 10/5/2019 15:31 Blue Royal Youth 1674 1 1 1 3297
323 2019 10/5/2019 15:31 Red Flamengo Esports 1674 0 0 0 -3297
334 2019 10/7/2019 10:33 Blue Lowkey Esports.Vietnam 2500 1 0 1 3745
335 2019 10/7/2019 10:33 Red DWG KIA 2500 0 1 0 -3745
346 2019 10/7/2019 12:17 Blue DWG KIA 1573 1 0 1 2952
347 2019 10/7/2019 12:17 Red Lowkey Esports.Vietnam 1573 0 1 0 -2952
358 2019 10/7/2019 12:49 Blue DWG KIA 1986 1 1 1 3048
359 2019 10/7/2019 12:49 Red Lowkey Esports.Vietnam 1986 0 0 0 -3048
370 2019 10/7/2019 13:42 Blue Lowkey Esports.Vietnam 1363 0 0 0 -8152
371 2019 10/7/2019 13:42 Red DWG KIA 1363 1 1 1 8152
382 2019 10/7/2019 14:29 Blue Clutch Gaming 1799 1 0 1 3475
383 2019 10/7/2019 14:29 Red Royal Youth 1799 0 1 0 -3475
394 2019 10/7/2019 15:21 Blue Clutch Gaming 1658 1 1 1 4591
395 2019 10/7/2019 15:21 Red Royal Youth 1658 0 0 0 -4591
406 2019 10/7/2019 16:10 Blue Clutch Gaming 2219 1 1 1 5457
407 2019 10/7/2019 16:10 Red Royal Youth 2219 0 0 0 -5457
418 2019 10/8/2019 10:42 Blue Hong Kong Attitude 1676 1 1 1 4659
419 2019 10/8/2019 10:42 Red Isurus 1676 0 0 0 -4659
430 2019 10/8/2019 11:40 Blue Isurus 1981 1 1 1 1371
431 2019 10/8/2019 11:40 Red Hong Kong Attitude 1981 0 0 0 -1371
442 2019 10/8/2019 12:33 Blue Hong Kong Attitude 2421 1 1 1 -778
443 2019 10/8/2019 12:33 Red Isurus 2421 0 0 0 778
454 2019 10/8/2019 13:34 Blue Isurus 2202 0 0 0 -3260
455 2019 10/8/2019 13:34 Red Hong Kong Attitude 2202 1 1 1 3260
466 2019 10/8/2019 14:38 Blue Unicorns of Love.CIS 2732 1 0 1 -1559
467 2019 10/8/2019 14:38 Red Splyce 2732 0 1 0 1559
478 2019 10/8/2019 15:47 Blue Splyce 1894 1 0 1 1412
479 2019 10/8/2019 15:47 Red Unicorns of Love.CIS 1894 0 1 0 -1412
490 2019 10/8/2019 16:39 Blue Splyce 1830 1 1 1 6238
491 2019 10/8/2019 16:39 Red Unicorns of Love.CIS 1830 0 0 0 -6238
502 2019 10/8/2019 17:30 Blue Unicorns of Love.CIS 2530 1 0 1 3528
503 2019 10/8/2019 17:30 Red Splyce 2530 0 1 0 -3528
514 2019 10/8/2019 18:32 Blue Splyce 2271 1 0 0 -253
515 2019 10/8/2019 18:32 Red Unicorns of Love.CIS 2271 0 1 1 253
526 2019 10/12/2019 10:54 Blue Fnatic 2143 0 0 0 -678
527 2019 10/12/2019 10:54 Red SK Telecom T1 2143 1 1 1 678
538 2019 10/12/2019 12:56 Blue Royal Never Give Up 1883 1 0 0 1424
539 2019 10/12/2019 12:56 Red Clutch Gaming 1883 0 1 1 -1424
550 2019 10/12/2019 14:03 Blue Invictus Gaming 1780 1 0 0 -579
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562 2019 10/12/2019 15:04 Blue DWG KIA 1865 0 0 1 -54
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598 2019 10/13/2019 11:05 Blue SK Telecom T1 2564 1 0 1 -480
599 2019 10/13/2019 11:05 Red Royal Never Give Up 2564 0 1 0 480
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623 2019 10/13/2019 13:57 Red Invictus Gaming 2027 1 1 0 -816
634 2019 10/13/2019 14:56 Blue ahq eSports Club 1656 0 0 0 -3132
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646 2019 10/13/2019 15:49 Blue Griffin 1788 0 0 1 -814
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719 2019 10/14/2019 17:01 Red Hong Kong Attitude 2271 0 1 1 -201
730 2019 10/14/2019 18:02 Blue G2 Esports 1477 1 1 0 2086
731 2019 10/14/2019 18:02 Red Cloud9 1477 0 0 1 -2086
742 2019 10/15/2019 12:39 Blue Royal Never Give Up 2181 1 0 1 1848
743 2019 10/15/2019 12:39 Red Fnatic 2181 0 1 0 -1848
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766 2019 10/15/2019 14:42 Blue FunPlus Phoenix 1413 1 1 1 7502
767 2019 10/15/2019 14:42 Red GAM Esports 1413 0 0 0 -7502
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922 2019 10/18/2019 14:30 Blue Hong Kong Attitude 1808 0 0 1 -96
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946 2019 10/18/2019 16:19 Blue Hong Kong Attitude 1676 0 0 0 -888
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1031 2019 10/19/2019 15:46 Red Clutch Gaming 1986 0 0 1 -3771
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1366 2019 11/3/2019 10:46 Blue SK Telecom T1 2073 0 0 1 1601
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1438 2019 11/10/2019 14:26 Blue FunPlus Phoenix 1822 1 1 1 1697
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142 2018 10/2/2018 12:26 Blue Ascension Gaming 1607 0 1 1 -2340
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154 2018 10/3/2018 7:32 Blue G-Rex 1814 1 0 0 3351
155 2018 10/3/2018 7:32 Red Gambit Esports 1814 0 1 1 -3351
166 2018 10/3/2018 8:39 Blue KaBuM! e-Sports 2110 1 1 1 2139
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178 2018 10/3/2018 9:42 Blue Kaos Latin Gamers 2088 0 1 0 660
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190 2018 10/3/2018 10:36 Blue KaBuM! e-Sports 2073 0 0 1 -764
191 2018 10/3/2018 10:36 Red Cloud9 2073 1 1 0 764
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239 2018 10/4/2018 7:43 Red G2 Esports 2119 1 1 1 659
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274 2018 10/4/2018 10:38 Blue Infinity Esports 2105 1 1 1 2237
275 2018 10/4/2018 10:38 Red Dire Wolves 2105 0 0 0 -2237
286 2018 10/4/2018 11:35 Blue G2 Esports 1599 1 1 0 5041
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298 2018 10/4/2018 12:24 Blue EDward Gaming 1447 1 1 1 3977
299 2018 10/4/2018 12:24 Red Dire Wolves 1447 0 0 0 -3977
310 2018 10/4/2018 13:16 Blue G2 Esports 1804 1 1 0 4585
311 2018 10/4/2018 13:16 Red SuperMassive Esports 1804 0 0 1 -4585
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382 2018 10/6/2018 8:15 Blue DetonatioN FocusMe 1662 0 0 0 -5568
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442 2018 10/7/2018 5:38 Blue G2 Esports 2549 1 0 1 1476
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478 2018 10/7/2018 8:38 Blue SuperMassive Esports 2238 0 1 1 3600
479 2018 10/7/2018 8:38 Red G-Rex 2238 1 0 0 -3600
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491 2018 10/7/2018 9:36 Red SuperMassive Esports 2244 0 1 0 -153
502 2018 10/7/2018 10:36 Blue SuperMassive Esports 2129 0 0 1 -2690
503 2018 10/7/2018 10:36 Red G-Rex 2129 1 1 0 2690
514 2018 10/10/2018 7:08 Blue KT Rolster 1973 1 0 1 593
515 2018 10/10/2018 7:08 Red Team Liquid 1973 0 1 0 -593
526 2018 10/10/2018 8:59 Blue EDward Gaming 2505 1 1 1 22
527 2018 10/10/2018 8:59 Red MAD Team 2505 0 0 0 -22
538 2018 10/10/2018 10:09 Blue Phong VÅ© Buffalo 1625 0 0 1 -1644
539 2018 10/10/2018 10:09 Red Flash Wolves 1625 1 1 0 1644
550 2018 10/10/2018 11:09 Blue Kwangdong Freecs 2162 0 1 0 70
551 2018 10/10/2018 11:09 Red G2 Esports 2162 1 0 0 -70
562 2018 10/10/2018 12:15 Blue Royal Never Give Up 1510 1 1 1 3416
563 2018 10/10/2018 12:15 Red Cloud9 1510 0 0 0 -3416
574 2018 10/10/2018 13:10 Blue Gen.G 2639 0 0 0 2408
575 2018 10/10/2018 13:10 Red Team Vitality 2639 1 1 1 -2408
586 2018 10/11/2018 7:00 Blue Flash Wolves 2522 1 0 1 29
587 2018 10/11/2018 7:00 Red Kwangdong Freecs 2522 0 1 0 -29
598 2018 10/11/2018 8:53 Blue Phong VÅ© Buffalo 2049 1 0 1 2918
599 2018 10/11/2018 8:53 Red G2 Esports 2049 0 1 0 -2918
610 2018 10/11/2018 9:53 Blue 100 Thieves 1629 0 0 1 -3963
611 2018 10/11/2018 9:53 Red Fnatic 1629 1 1 0 3963
622 2018 10/11/2018 10:48 Blue Invictus Gaming 1674 1 0 0 1622
623 2018 10/11/2018 10:48 Red G-Rex 1674 0 1 1 -1622
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635 2018 10/11/2018 11:44 Red Cloud9 2853 1 1 0 -538
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647 2018 10/11/2018 13:02 Red Royal Never Give Up 2128 1 0 1 366
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670 2018 10/12/2018 8:34 Blue Team Liquid 1664 0 1 0 -941
671 2018 10/12/2018 8:34 Red EDward Gaming 1664 1 0 0 941
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706 2018 10/12/2018 11:19 Blue Royal Never Give Up 1745 1 1 1 1638
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718 2018 10/12/2018 12:09 Blue Cloud9 1481 0 0 1 -882
719 2018 10/12/2018 12:09 Red Gen.G 1481 1 1 0 882
730 2018 10/13/2018 7:19 Blue Kwangdong Freecs 1587 1 1 1 2022
731 2018 10/13/2018 7:19 Red Phong VÅ© Buffalo 1587 0 0 0 -2022
742 2018 10/13/2018 8:36 Blue G2 Esports 1898 1 0 0 1449
743 2018 10/13/2018 8:36 Red Flash Wolves 1898 0 1 0 -1449
754 2018 10/13/2018 9:34 Blue Invictus Gaming 1226 1 1 1 2242
755 2018 10/13/2018 9:34 Red 100 Thieves 1226 0 0 0 -2242
766 2018 10/13/2018 10:23 Blue G-Rex 1447 0 0 0 -3771
767 2018 10/13/2018 10:23 Red Fnatic 1447 1 1 1 3771
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791 2018 10/13/2018 12:13 Red EDward Gaming 2066 0 0 1 -3098
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1391 2017 10/29/2017 10:23 Red Team WE 1929 0 1 0 9
1402 2017 11/4/2017 6:52 Blue Samsung Galaxy 2243 1 1 1 1731
1403 2017 11/4/2017 6:52 Red SK Telecom T1 2243 0 0 0 -1731
1414 2017 11/4/2017 8:10 Blue Samsung Galaxy 2079 1 0 0 -1354
1415 2017 11/4/2017 8:10 Red SK Telecom T1 2079 0 1 1 1354
1426 2017 11/4/2017 9:04 Blue Samsung Galaxy 2421 1 0 0 -1171
1427 2017 11/4/2017 9:04 Red SK Telecom T1 2421 0 1 1 1171
10 2016 9/29/2016 22:28 Blue G2 Esports 2053 0 0 0 -1956
11 2016 9/29/2016 22:28 Red Counter Logic Gaming 2053 1 1 0 1956
22 2016 9/30/2016 0:20 Blue ROX Tigers 1656 1 1 0 2836
23 2016 9/30/2016 0:20 Red Albus NoX Luna 1656 0 0 0 -2836
34 2016 9/30/2016 1:15 Blue H2k-Gaming 2831 0 0 0 1103
35 2016 9/30/2016 1:15 Red ahq eSports Club 2831 1 1 0 -1103
46 2016 9/30/2016 2:35 Blue INTZ 2531 1 0 1 3427
47 2016 9/30/2016 2:35 Red EDward Gaming 2531 0 1 0 -3427
58 2016 9/30/2016 3:45 Blue Samsung Galaxy 2039 1 1 0 4006
59 2016 9/30/2016 3:45 Red Splyce 2039 0 0 0 -4006
70 2016 9/30/2016 5:32 Blue TSM 2222 0 1 0 1426
71 2016 9/30/2016 5:32 Red Royal Never Give Up 2222 1 0 0 -1426
82 2016 9/30/2016 22:00 Blue SK Telecom T1 2050 1 0 0 1477
83 2016 9/30/2016 22:00 Red Cloud9 2050 0 1 0 -1477
94 2016 9/30/2016 23:59 Blue Flash Wolves 2839 0 0 1 2943
95 2016 9/30/2016 23:59 Red I May 2839 1 1 0 -2943
106 2016 10/1/2016 1:12 Blue TSM 2037 1 1 0 6930
107 2016 10/1/2016 1:12 Red Samsung Galaxy 2037 0 0 0 -6930
118 2016 10/1/2016 2:17 Blue Royal Never Give Up 1532 1 1 0 4968
119 2016 10/1/2016 2:17 Red Splyce 1532 0 0 0 -4968
130 2016 10/1/2016 3:12 Blue H2k-Gaming 1841 1 1 0 2225
131 2016 10/1/2016 3:12 Red INTZ 1841 0 0 0 -2225
142 2016 10/1/2016 4:06 Blue EDward Gaming 2461 1 0 1 914
143 2016 10/1/2016 4:06 Red ahq eSports Club 2461 0 1 0 -914
154 2016 10/1/2016 21:06 Blue Counter Logic Gaming 2395 0 0 0 -1484
155 2016 10/1/2016 21:06 Red Albus NoX Luna 2395 1 1 0 1484
166 2016 10/1/2016 23:58 Blue ROX Tigers 2619 1 0 0 -4364
167 2016 10/1/2016 23:58 Red G2 Esports 2619 0 1 0 4364
178 2016 10/2/2016 1:16 Blue Flash Wolves 4220 0 0 0 2572
179 2016 10/2/2016 1:16 Red Cloud9 4220 1 1 0 -2572
190 2016 10/2/2016 2:55 Blue I May 1913 0 0 0 -3320
191 2016 10/2/2016 2:55 Red SK Telecom T1 1913 1 1 0 3320
202 2016 10/2/2016 3:54 Blue Splyce 2702 0 0 0 1022
203 2016 10/2/2016 3:54 Red TSM 2702 1 1 0 -1022
214 2016 10/2/2016 5:02 Blue Royal Never Give Up 1938 0 1 0 390
215 2016 10/2/2016 5:02 Red Samsung Galaxy 1938 1 0 0 -390
226 2016 10/2/2016 21:59 Blue ahq eSports Club 2114 1 1 0 4758
227 2016 10/2/2016 21:59 Red INTZ 2114 0 0 0 -4758
238 2016 10/2/2016 23:57 Blue EDward Gaming 2277 1 1 1 3501
239 2016 10/2/2016 23:57 Red H2k-Gaming 2277 0 0 0 -3501
250 2016 10/3/2016 1:01 Blue Albus NoX Luna 2424 1 1 0 887
251 2016 10/3/2016 1:01 Red G2 Esports 2424 0 0 0 -887
262 2016 10/3/2016 2:09 Blue Counter Logic Gaming 2194 1 0 0 2733
263 2016 10/3/2016 2:09 Red ROX Tigers 2194 0 1 0 -2733
274 2016 10/3/2016 3:39 Blue SK Telecom T1 2496 0 1 0 -1416
275 2016 10/3/2016 3:39 Red Flash Wolves 2496 1 0 0 1416
286 2016 10/3/2016 4:26 Blue Cloud9 2317 1 0 0 3267
287 2016 10/3/2016 4:26 Red I May 2317 0 1 0 -3267
298 2016 10/6/2016 22:55 Blue G2 Esports 2124 0 1 0 -16
299 2016 10/6/2016 22:55 Red ROX Tigers 2124 1 0 0 16
310 2016 10/6/2016 23:58 Blue Albus NoX Luna 1802 1 1 1 997
311 2016 10/6/2016 23:58 Red Counter Logic Gaming 1802 0 0 0 -997
322 2016 10/7/2016 0:53 Blue Counter Logic Gaming 2177 1 1 0 2659
323 2016 10/7/2016 0:53 Red G2 Esports 2177 0 0 0 -2659
334 2016 10/7/2016 2:05 Blue Albus NoX Luna 3980 1 1 0 1040
335 2016 10/7/2016 2:05 Red ROX Tigers 3980 0 0 0 -1040
346 2016 10/7/2016 3:28 Blue G2 Esports 2202 1 0 0 -802
347 2016 10/7/2016 3:28 Red Albus NoX Luna 2202 0 1 0 802
358 2016 10/7/2016 4:30 Blue ROX Tigers 2266 1 0 0 354
359 2016 10/7/2016 4:30 Red Counter Logic Gaming 2266 0 1 0 -354
370 2016 10/7/2016 5:36 Blue ROX Tigers 1762 1 1 1 4371
371 2016 10/7/2016 5:36 Red Albus NoX Luna 1762 0 0 0 -4371
382 2016 10/7/2016 22:51 Blue EDward Gaming 2081 1 1 0 3743
383 2016 10/7/2016 22:51 Red INTZ 2081 0 0 0 -3743
394 2016 10/7/2016 23:58 Blue ahq eSports Club 2502 0 0 0 -1632
395 2016 10/7/2016 23:58 Red H2k-Gaming 2502 1 1 0 1632
406 2016 10/8/2016 1:03 Blue H2k-Gaming 2697 1 1 0 3446
407 2016 10/8/2016 1:03 Red EDward Gaming 2697 0 0 0 -3446
418 2016 10/8/2016 2:13 Blue INTZ 1709 0 0 0 -1335
419 2016 10/8/2016 2:13 Red ahq eSports Club 1709 1 1 0 1335
430 2016 10/8/2016 3:03 Blue INTZ 1756 0 0 0 -1336
431 2016 10/8/2016 3:03 Red H2k-Gaming 1756 1 1 0 1336
442 2016 10/8/2016 3:56 Blue ahq eSports Club 2815 0 1 0 2806
443 2016 10/8/2016 3:56 Red EDward Gaming 2815 1 0 0 -2806
454 2016 10/8/2016 5:17 Blue H2k-Gaming 2692 1 1 0 2887
455 2016 10/8/2016 5:17 Red EDward Gaming 2692 0 0 0 -2887
466 2016 10/8/2016 19:03 Blue Samsung Galaxy 2711 1 0 0 -1257
467 2016 10/8/2016 19:03 Red TSM 2711 0 1 0 1257
478 2016 10/8/2016 21:04 Blue Splyce 2175 1 1 0 2740
479 2016 10/8/2016 21:04 Red Royal Never Give Up 2175 0 0 0 -2740
490 2016 10/8/2016 22:05 Blue TSM 1715 1 1 0 2637
491 2016 10/8/2016 22:05 Red Splyce 1715 0 0 0 -2637
502 2016 10/8/2016 22:56 Blue Samsung Galaxy 1433 1 1 0 7534
503 2016 10/8/2016 22:56 Red Royal Never Give Up 1433 0 0 0 -7534
514 2016 10/8/2016 23:42 Blue Splyce 1422 0 0 0 -5083
515 2016 10/8/2016 23:42 Red Samsung Galaxy 1422 1 1 0 5083
526 2016 10/9/2016 0:25 Blue Royal Never Give Up 2212 1 1 0 1874
527 2016 10/9/2016 0:25 Red TSM 2212 0 0 0 -1874
538 2016 10/9/2016 18:59 Blue I May 2471 1 0 0 -805
539 2016 10/9/2016 18:59 Red Flash Wolves 2471 0 1 0 805
550 2016 10/9/2016 20:59 Blue Cloud9 3010 0 1 0 1172
551 2016 10/9/2016 20:59 Red SK Telecom T1 3010 1 0 0 -1172
562 2016 10/9/2016 22:16 Blue Cloud9 2388 0 0 0 -105
563 2016 10/9/2016 22:16 Red Flash Wolves 2388 1 1 0 105
574 2016 10/9/2016 23:23 Blue SK Telecom T1 2727 1 1 0 3716
575 2016 10/9/2016 23:23 Red I May 2727 0 0 0 -3716
586 2016 10/10/2016 0:34 Blue I May 3067 0 0 0 -5273
587 2016 10/10/2016 0:34 Red Cloud9 3067 1 1 0 5273
598 2016 10/10/2016 1:49 Blue Flash Wolves 2391 0 0 0 -1396
599 2016 10/10/2016 1:49 Red SK Telecom T1 2391 1 1 0 1396
610 2016 10/13/2016 21:50 Blue Samsung Galaxy 1651 1 1 1 1102
611 2016 10/13/2016 21:50 Red Cloud9 1651 0 0 0 -1102
622 2016 10/13/2016 22:55 Blue Cloud9 2281 0 1 0 520
623 2016 10/13/2016 22:55 Red Samsung Galaxy 2281 1 0 0 -520
634 2016 10/14/2016 0:00 Blue Samsung Galaxy 2388 1 1 0 -449
635 2016 10/14/2016 0:00 Red Cloud9 2388 0 0 0 449
646 2016 10/14/2016 21:42 Blue SK Telecom T1 2820 0 0 0 -2644
647 2016 10/14/2016 21:42 Red Royal Never Give Up 2820 1 1 0 2644
658 2016 10/14/2016 23:21 Blue Royal Never Give Up 2586 0 0 0 -4218
659 2016 10/14/2016 23:21 Red SK Telecom T1 2586 1 1 1 4218
670 2016 10/15/2016 0:31 Blue SK Telecom T1 1812 1 0 0 5331
671 2016 10/15/2016 0:31 Red Royal Never Give Up 1812 0 1 0 -5331
682 2016 10/15/2016 1:30 Blue Royal Never Give Up 1843 0 0 0 1242
683 2016 10/15/2016 1:30 Red SK Telecom T1 1843 1 1 0 -1242
694 2016 10/15/2016 21:21 Blue ROX Tigers 2144 1 1 0 1842
695 2016 10/15/2016 21:21 Red EDward Gaming 2144 0 0 0 -1842
706 2016 10/15/2016 23:04 Blue EDward Gaming 1568 0 0 0 -3933
707 2016 10/15/2016 23:04 Red ROX Tigers 1568 1 1 0 3933
718 2016 10/15/2016 23:54 Blue ROX Tigers 2088 0 1 0 -1410
719 2016 10/15/2016 23:54 Red EDward Gaming 2088 1 0 0 1410
730 2016 10/16/2016 0:51 Blue EDward Gaming 1954 0 0 0 -1156
731 2016 10/16/2016 0:51 Red ROX Tigers 1954 1 1 0 1156
742 2016 10/16/2016 21:39 Blue H2k-Gaming 2084 1 0 1 4506
743 2016 10/16/2016 21:39 Red Albus NoX Luna 2084 0 1 0 -4506
754 2016 10/16/2016 23:07 Blue Albus NoX Luna 1418 0 0 0 -7465
755 2016 10/16/2016 23:07 Red H2k-Gaming 1418 1 1 1 7465
766 2016 10/16/2016 23:56 Blue H2k-Gaming 2092 1 1 0 5286
767 2016 10/16/2016 23:56 Red Albus NoX Luna 2092 0 0 1 -5286
778 2016 10/21/2016 21:50 Blue ROX Tigers 2616 0 1 0 -1713
779 2016 10/21/2016 21:50 Red SK Telecom T1 2616 1 0 0 1713
790 2016 10/21/2016 23:13 Blue SK Telecom T1 2002 0 0 1 -2020
791 2016 10/21/2016 23:13 Red ROX Tigers 2002 1 1 0 2020
802 2016 10/22/2016 0:13 Blue ROX Tigers 2360 1 0 0 2571
803 2016 10/22/2016 0:13 Red SK Telecom T1 2360 0 1 0 -2571
814 2016 10/22/2016 1:14 Blue SK Telecom T1 2577 1 0 0 809
815 2016 10/22/2016 1:14 Red ROX Tigers 2577 0 1 0 -809
826 2016 10/22/2016 2:23 Blue ROX Tigers 2548 0 0 0 -551
827 2016 10/22/2016 2:23 Red SK Telecom T1 2548 1 1 0 551
838 2016 10/22/2016 20:50 Blue Samsung Galaxy 2361 1 1 0 1611
839 2016 10/22/2016 20:50 Red H2k-Gaming 2361 0 0 0 -1611
850 2016 10/22/2016 23:12 Blue H2k-Gaming 2068 0 0 0 839
851 2016 10/22/2016 23:12 Red Samsung Galaxy 2068 1 1 0 -839
862 2016 10/23/2016 0:06 Blue Samsung Galaxy 1605 1 1 1 5057
863 2016 10/23/2016 0:06 Red H2k-Gaming 1605 0 0 0 -5057
874 2016 10/29/2016 21:53 Blue Samsung Galaxy 3281 0 1 0 -3958
875 2016 10/29/2016 21:53 Red SK Telecom T1 3281 1 0 0 3958
886 2016 10/30/2016 0:32 Blue SK Telecom T1 1886 1 0 1 1283
887 2016 10/30/2016 0:32 Red Samsung Galaxy 1886 0 1 0 -1283
898 2016 10/30/2016 1:31 Blue Samsung Galaxy 4280 1 1 0 -1555
899 2016 10/30/2016 1:31 Red SK Telecom T1 4280 0 0 0 1555
910 2016 10/30/2016 3:04 Blue SK Telecom T1 2761 0 0 0 -136
911 2016 10/30/2016 3:04 Red Samsung Galaxy 2761 1 1 0 136
922 2016 10/30/2016 4:13 Blue Samsung Galaxy 2951 0 0 0 508
923 2016 10/30/2016 4:13 Red SK Telecom T1 2951 1 1 0 -508
In [12]:
#Checking the features of the combined data
CombinedData.shape
Out[12]:
(1620, 9)
In [13]:
#Ensuring that the combined data has no missing values
CombinedData.isna().sum()
Out[13]:
year            0
date            0
side            0
teamname        0
gamelength      0
result          0
firstdragon     0
firstherald     0
golddiffat15    0
dtype: int64
In [14]:
#Checking the data types for each column
CombinedData.dtypes
Out[14]:
year            object
date            object
side            object
teamname        object
gamelength      object
result          object
firstdragon     object
firstherald     object
golddiffat15    object
dtype: object

5. Exploratory Data Analysis

5.1 Gamelength of World Championship Games

In [15]:
#Changing the types of the columns to be able to use for my graph plotting
CombinedData['year']=CombinedData['year'].astype(int)
CombinedData['gamelength']=CombinedData['gamelength'].astype(int)/60
CombinedData['date']= pd.to_datetime(CombinedData['date'], format = '%m/%d/%Y %H:%M')
CombinedData['date']= pd.to_datetime(CombinedData['date'].astype(int))

#Plotting the graph using plotly
CombinedDataPlot = px.scatter(CombinedData, x = 'date', y = 'gamelength', color = 'year', 
                              title = "Gamelength of World Championship Games", color_continuous_scale = px.colors.sequential.Bluered,
                              labels = {
                                  'date': "Year",
                                  'gamelength': "Game Length (Minutes)",
                                  'year': "Year"
                              })
CombinedDataPlot.show()
From the scatterplot above, we are able to see that in 2016, the games are longer, with some games being over 60 minutes. In the later years, the games do not last as long, with the latest year's (2022) games mostly being around 20-40 minutes.

5.2 The Win Rate for the different sides every year

As the data contains every game played, the total games played will be half of the total, as for each game played, there will be both a red and blue side. Therefore we will need to take half of the total games played.
In [16]:
#Creating a function to obtain the side win rate for the year's data.
def FindSideWinRatePerYear(data, color):
    
    #Obtaining the total games played
    TotalGamesPlayed = (len(data)/2)
    
    #Filtering the data to the side, with the games won
    sideWins = len(data[(data['side'] == color) & (data['result'] == '1')])
    
    #Calculate the winrate for the side
    sideWinRate = float((sideWins/TotalGamesPlayed))

    #Returning the winrate value
    return sideWinRate

#Saving the side win rate for the year's data to each variable
BlueSideWinRate2022 = FindSideWinRatePerYear(CleanedData2022, 'Blue')
RedSideWinRate2022 = FindSideWinRatePerYear(CleanedData2022, 'Red')

BlueSideWinRate2021 = FindSideWinRatePerYear(CleanedData2021, 'Blue')
RedSideWinRate2021 = FindSideWinRatePerYear(CleanedData2021, 'Red')

BlueSideWinRate2020 = FindSideWinRatePerYear(CleanedData2020, 'Blue')
RedSideWinRate2020 = FindSideWinRatePerYear(CleanedData2020, 'Red')

BlueSideWinRate2019 = FindSideWinRatePerYear(CleanedData2019, 'Blue')
RedSideWinRate2019 = FindSideWinRatePerYear(CleanedData2019, 'Red')

BlueSideWinRate2018 = FindSideWinRatePerYear(CleanedData2018, 'Blue')
RedSideWinRate2018 = FindSideWinRatePerYear(CleanedData2018, 'Red')

BlueSideWinRate2017 = FindSideWinRatePerYear(CleanedData2017, 'Blue')
RedSideWinRate2017 = FindSideWinRatePerYear(CleanedData2017, 'Red')

BlueSideWinRate2016 = FindSideWinRatePerYear(CleanedData2016, 'Blue')
RedSideWinRate2016 = FindSideWinRatePerYear(CleanedData2016, 'Red')

5.2.1 Creating a new dataframe

In [17]:
#Creating a new dataframe with the side win rates obtained from earlier function
winRateData=pd.DataFrame({"Year":['2016','2016', '2017', '2017', '2018', '2018', 
                         '2019', '2019', '2020', '2020', '2021', '2021', 
                         '2022', '2022'],
                 "Side":['Blue', 'Red', 'Blue', 'Red', 'Blue', 'Red', 'Blue', 'Red', 'Blue', 'Red',
                         'Blue', 'Red', 'Blue', 'Red'],
                 "WinRate": [BlueSideWinRate2016, RedSideWinRate2016, 
                             BlueSideWinRate2017, RedSideWinRate2017, 
                             BlueSideWinRate2018, RedSideWinRate2018, 
                             BlueSideWinRate2019, RedSideWinRate2019, 
                             BlueSideWinRate2020, RedSideWinRate2020, 
                             BlueSideWinRate2021, RedSideWinRate2021, 
                             BlueSideWinRate2022, RedSideWinRate2022, ]})

winRateData
Out[17]:
Year Side WinRate
0 2016 Blue 0.545455
1 2016 Red 0.454545
2 2017 Blue 0.563025
3 2017 Red 0.436975
4 2018 Blue 0.571429
5 2018 Red 0.428571
6 2019 Blue 0.521008
7 2019 Red 0.478992
8 2020 Blue 0.508772
9 2020 Red 0.491228
10 2021 Blue 0.487603
11 2021 Red 0.512397
12 2022 Blue 0.531915
13 2022 Red 0.468085
In [18]:
#Plotting the side win rate using a histogram
sideWinRatePlot = px.histogram(winRateData, x = 'Year', y = 'WinRate', color = 'Side', barmode = 'group', 
                     title = "Win Rate Per Side Comparison from 2016-2022", 
                           text_auto = '.1%', height = 500).update_layout(yaxis_title="Win Rate (%)")  
sideWinRatePlot.layout.yaxis.tickformat = '.0%'
sideWinRatePlot.show()

From this histogram, we are able to see that from the years 2016-2022, for the World Championship games played, the Blue side has a higher Win Rate as compared to the Red side, with the exception of 2021, where the red side has a slightly higher Win Rate.

5.3 The Win Rate with first objectives taken over the years

In [19]:
#Creating a function to obtain the winrate with first objectives taken
def FindWinRateWithFirstObjectivesTaken(data):
    
    #Obtaining the total games played for the year
    TotalGamesPlayed = (len(data)/2)
    
    #Obtaining the wins with the first dragon taken
    WinsWithFirstDragonTaken = len(data[(data['firstdragon'] == '1') & (data['result'] == '1')])
    
    #Obtaining the wins with the first herald taken
    WinsWithFirstHeraldTaken = len(data[(data['firstherald'] == '1') & (data['result'] == '1')])
    
    #Obtaining the wins with both first objectives taken
    WinsWithBothFirstObjectivesTaken = len(data[(data['firstdragon'] == '1') & (data['firstherald'] == '1') & 
                                                (data['result'] == '1')])
    
    YearOfGamesPlayed = data['year'].iloc[0]
    
    #Obtaining the winrate with the respective objectives taken
    WinRateWithFirstDragonTaken = float(WinsWithFirstDragonTaken/TotalGamesPlayed)
    WinRateWithFirstHeraldTaken = float(WinsWithFirstHeraldTaken/TotalGamesPlayed)
    WinRateWithBothFirstObjectivesTaken = float(WinsWithBothFirstObjectivesTaken/TotalGamesPlayed)
    
    #returning the values in a list
    return [(YearOfGamesPlayed, WinRateWithFirstDragonTaken, "First Dragon"), (YearOfGamesPlayed, WinRateWithFirstHeraldTaken, "First Herald"), (YearOfGamesPlayed, WinRateWithBothFirstObjectivesTaken, "Both Objectives")]
    
#Saving the winrates to newly created variables    
WinRateWithFirstObjectivesTaken2022 = FindWinRateWithFirstObjectivesTaken(CleanedData2022)
WinRateWithFirstObjectivesTaken2021 = FindWinRateWithFirstObjectivesTaken(CleanedData2021)
WinRateWithFirstObjectivesTaken2020 = FindWinRateWithFirstObjectivesTaken(CleanedData2020)
WinRateWithFirstObjectivesTaken2019 = FindWinRateWithFirstObjectivesTaken(CleanedData2019)
WinRateWithFirstObjectivesTaken2018 = FindWinRateWithFirstObjectivesTaken(CleanedData2018)
WinRateWithFirstObjectivesTaken2017 = FindWinRateWithFirstObjectivesTaken(CleanedData2017)
WinRateWithFirstObjectivesTaken2016 = FindWinRateWithFirstObjectivesTaken(CleanedData2016)


#Showing how it looks like right now
print(WinRateWithFirstObjectivesTaken2022)
print(WinRateWithFirstObjectivesTaken2021)
print(WinRateWithFirstObjectivesTaken2020)
print(WinRateWithFirstObjectivesTaken2019)
print(WinRateWithFirstObjectivesTaken2018)
print(WinRateWithFirstObjectivesTaken2017)
print(WinRateWithFirstObjectivesTaken2016)

[('2022', 0.5886524822695035, 'First Dragon'), ('2022', 0.5886524822695035, 'First Herald'), ('2022', 0.3546099290780142, 'Both Objectives')]
[('2021', 0.5702479338842975, 'First Dragon'), ('2021', 0.5702479338842975, 'First Herald'), ('2021', 0.3305785123966942, 'Both Objectives')]
[('2020', 0.5701754385964912, 'First Dragon'), ('2020', 0.5614035087719298, 'First Herald'), ('2020', 0.2631578947368421, 'Both Objectives')]
[('2019', 0.6302521008403361, 'First Dragon'), ('2019', 0.5882352941176471, 'First Herald'), ('2019', 0.36134453781512604, 'Both Objectives')]
[('2018', 0.5798319327731093, 'First Dragon'), ('2018', 0.6554621848739496, 'First Herald'), ('2018', 0.37815126050420167, 'Both Objectives')]
[('2017', 0.6134453781512605, 'First Dragon'), ('2017', 0.5966386554621849, 'First Herald'), ('2017', 0.3697478991596639, 'Both Objectives')]
[('2016', 0.6753246753246753, 'First Dragon'), ('2016', 0.14285714285714285, 'First Herald'), ('2016', 0.09090909090909091, 'Both Objectives')]
In [20]:
#Combining the lists together
WinRateWithFirstObjectivesTakenCombined = WinRateWithFirstObjectivesTaken2016 + WinRateWithFirstObjectivesTaken2017 + WinRateWithFirstObjectivesTaken2018 + WinRateWithFirstObjectivesTaken2019 + WinRateWithFirstObjectivesTaken2020 + WinRateWithFirstObjectivesTaken2021 + WinRateWithFirstObjectivesTaken2022

#Showing how the combined list looks like
print(WinRateWithFirstObjectivesTakenCombined)

[('2016', 0.6753246753246753, 'First Dragon'), ('2016', 0.14285714285714285, 'First Herald'), ('2016', 0.09090909090909091, 'Both Objectives'), ('2017', 0.6134453781512605, 'First Dragon'), ('2017', 0.5966386554621849, 'First Herald'), ('2017', 0.3697478991596639, 'Both Objectives'), ('2018', 0.5798319327731093, 'First Dragon'), ('2018', 0.6554621848739496, 'First Herald'), ('2018', 0.37815126050420167, 'Both Objectives'), ('2019', 0.6302521008403361, 'First Dragon'), ('2019', 0.5882352941176471, 'First Herald'), ('2019', 0.36134453781512604, 'Both Objectives'), ('2020', 0.5701754385964912, 'First Dragon'), ('2020', 0.5614035087719298, 'First Herald'), ('2020', 0.2631578947368421, 'Both Objectives'), ('2021', 0.5702479338842975, 'First Dragon'), ('2021', 0.5702479338842975, 'First Herald'), ('2021', 0.3305785123966942, 'Both Objectives'), ('2022', 0.5886524822695035, 'First Dragon'), ('2022', 0.5886524822695035, 'First Herald'), ('2022', 0.3546099290780142, 'Both Objectives')]

5.3.1 Creating another dataframe

In [21]:
#Creating a new dataframe using the combined list
FirstObjectivesTakenData = pd.DataFrame(WinRateWithFirstObjectivesTakenCombined, columns = ['Year', 'Win Rate', 'Objectives'])

#Showing how the dataframe looks like
FirstObjectivesTakenData
Out[21]:
Year Win Rate Objectives
0 2016 0.675325 First Dragon
1 2016 0.142857 First Herald
2 2016 0.090909 Both Objectives
3 2017 0.613445 First Dragon
4 2017 0.596639 First Herald
5 2017 0.369748 Both Objectives
6 2018 0.579832 First Dragon
7 2018 0.655462 First Herald
8 2018 0.378151 Both Objectives
9 2019 0.630252 First Dragon
10 2019 0.588235 First Herald
11 2019 0.361345 Both Objectives
12 2020 0.570175 First Dragon
13 2020 0.561404 First Herald
14 2020 0.263158 Both Objectives
15 2021 0.570248 First Dragon
16 2021 0.570248 First Herald
17 2021 0.330579 Both Objectives
18 2022 0.588652 First Dragon
19 2022 0.588652 First Herald
20 2022 0.354610 Both Objectives
In [22]:
#Plotting a histogram for the dataframe above
FirstObjectivesTakenWinRatePlot = px.histogram(FirstObjectivesTakenData, x = 'Year', y = 'Win Rate', color = 'Objectives', barmode = 'group', 
                     title = "First Objectives Taken Win Rate from 2016-2022", 
                           text_auto = '.1%', height = 500).update_layout(yaxis_title="Win Rate (%)", yaxis_tickformat='%') 
FirstObjectivesTakenWinRatePlot.layout.yaxis.tickformat = '.0%'
FirstObjectivesTakenWinRatePlot.show()
From the histogram plotted above, we are able to see that the win rate for teams that takes the First Dragon in 2016 is a lot higher than when the First Herald is taken. Moving on to around 2018 is when the win rate for teams that takes the First Herald is higher than that of the First Dragon. In the latest year, 2022, we can see that the win rate for teams that takes the first dragon is the same as those that takes the first herald. The win rate for teams that takes both first dragon and herald is not that high because the amount of games whereby the teams take both first objectives is low compared to the total games played.

5.4 Gold Differential At 15 Minutes for Games Won each year

In [23]:
GamesWonByYear = CombinedData[CombinedData['result'] == '1']

#Changing the object to an integer, to be able to use the values to plot the graph 
GamesWonByYear.loc[:, 'golddiffat15']=GamesWonByYear['golddiffat15'].astype(int)

#Plotting the graph
GoldDiffAt15Plot = px.scatter(GamesWonByYear, x = 'date', y = 'golddiffat15', color = 'year', 
                              title = "Gold Differential at 15 Minutes for Games Won", color_continuous_scale = px.colors.sequential.Bluered,
                              labels = {
                                  'date': "Year",
                                  'golddiffat15': "Gold Differential at 15 Minutes",
                                  'year': "Year"
                              })
    
GoldDiffAt15Plot.show()

From the plot above, we are able to see that there is quite a big range for the gold differential at 15 minutes for the games won every year. This shows that even though earlier on we saw that the game length for the later years are much shorter compared to the earlier years, the gold differential at 15 minutes does not really matter as much, and there are teams that win with a gold deficit at 15 minutes.

6. Findings and Conclusion

6.1 Findings

In this project, I have plotting 4 different diagrams each showing a different metric to aid with my analysis. Firstly, I have found that around 2016, the games are generally longer as compared to now, where most games are around the 20-40 minutes mark. Secondly, the win rate for the blue side team is usually higher as compared to the red side. Thirdly, The win rate for the team that takes the first dragon was higher than that of the teams that took the first herald, but in this year, the win rate is the same regardless of which objective is taken. Lastly, the gold differential at 15 minutes even though it does play a part, there is still a chance for the team that won to have a gold deficit at 15 minutes.

6.2 Conclusion

In conclusion, I have achieved my aim of complete my goal of exploring the data for League Of Legends' World Championship games played from 2016-2022. With these analysis done, I can infer from it that Riot Games is making the games to be much shorter, with more focus on balancing the game objectives, such that regardless of which first objectives is taken, the win rate is about the same. Also, the gold deficit will not be that detrimental to the team such that there is no chance of a come back. With these data, and more time to do further research into this topic, we can probably gather even more data from previous years, and be able to predict in more detail what league games in the future will be like.

7. References and Resources

7.1 References

    Example Report on Programming with Data Coursera
    How to play - league of legends. leagueoflegends.com. (n.d.) [Online]. Available: https://www.leagueoflegends.com/en-us/how-to-play/
    Wikimedia Foundation. (2022, December 30). League of Legends. Wikipedia. (n.d.). [Online]. Available: https://en.wikipedia.org/wiki/League_of_Legends
    Riot Games API Policies. Riot Developer Portal. (n.d.) https://developer.riotgames.com/policies/general
    Oracle's Elixir - Lol Esports stats. Oracle's Elixir - LoL Esports Stats. (n.d.). [Online]. Available: https://oracleselixir.com/about

7.2 Resources

Datasets

    Oracle's Elixir - Lol Esports stats. Oracle's Elixir - LoL Esports Stats (n.d.). [Online]. Available: https://oracleselixir.com/tools/downloads

Images

    Gamers, G. (2017, October 4). Team we vs. young generation 2017 World Championship - play-in round 2 post-match discussion. GosuGamers (n.d.). [Online]. Available: https://www.gosugamers.net/lol/reddit/45431-team-we-vs-young-generation-2017-world-championship-play-in-round-2-post-match-discussion

Data Cleaning

    Create a pandas DataFrame from lists. GeeksforGeeks [Online] (2022, July 7). Available: https://www.geeksforgeeks.org/create-a-pandas-dataframe-from-lists/
    Merge, join, Concatenate and compare. Merge, join, concatenate and compare - pandas 1.5.2 documentation (n.d.). [Online]. Available: https://pandas.pydata.org/docs/user_guide/merging.html
    Working with missing data in Pandas. GeeksforGeeks. (2022, June 8). [Online]. Available: https://www.geeksforgeeks.org/working-with-missing-data-in-pandas/
    Pandas.to_datetime. pandas.to_datetime - pandas 1.5.2 documentation. (n.d.). [Online]. Available: https://pandas.pydata.org/docs/reference/api/pandas.to_datetime.html
    Nnk. (2022, December 7). Pandas - replace Nan values with zero in a column. Spark by {Examples}. [Online]. Available: https://sparkbyexamples.com/pandas/pandas-replace-nan-values-by-zero-in-a-column/#:~:text=Replace%20NaN%20Values%20with%20Zero%20on%20pandas%20DataFrame,but%20returns%20a%20new%20DataFrame.

Exploratory Data Analysis

    Plotly. Plotly express in Python. (n.d.). [Online]. Available: https://plotly.com/python/plotly-express/#scatter-line-area-and-bar-charts
    Built-in continuous color scales in python plotly. GeeksforGeeks. (2022, February 28). [Online]. Available: https://www.geeksforgeeks.org/built-in-continuous-color-scales-in-python-plotly/
    Setting the Font, Title, Legend Entries, and Axis Titles in Python. Setting the font, title, legend entries, and axis titles in Python. (n.d.). [Online]. Available: https://plotly.com/python/figure-labels/